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The next AI database isn’t another database. it’s a layer over the one you already have. Today we're launching a virtual ai search layer you can deploy over ANY Postgres database. no migrations. no vector DB. no Neo4j. no sync. no bs. One database. Every way to search it.... show more

dale

4,021 subscribers

18,108 Aufrufe • vor 1 Monat •via X (Twitter)

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58 Kommentare

Profilbild von dale

dalevor 1 Monat

@damienhe @polygres @fdotinc @grok wdyt :D

Profilbild von dale

dalevor 1 Monat

Biggest update yet our team was cooking this last week 🫡 Back to work

Profilbild von stavan

stavanvor 1 Monat

@daltonmeon @damienhe @polygres @fdotinc 👉- - - - | | | | - - - - 👈

Profilbild von dale

dalevor 1 Monat

@daltonmeon @damienhe @polygres @fdotinc lol

Profilbild von Shengkun Ye

Shengkun Yevor 1 Monat

@daltonmeon @damienhe @polygres @fdotinc this is huge!!

Profilbild von dale

dalevor 1 Monat

@daltonmeon @damienhe @polygres @fdotinc Thank you bro!

Profilbild von Shakil

Shakilvor 1 Monat

@daltonmeon @damienhe @polygres @fdotinc Waiting to try it

Profilbild von dale

dalevor 1 Monat

@daltonmeon @damienhe @polygres @fdotinc Try it rn!

Profilbild von Manny Hernandez

Manny Hernandezvor 1 Monat

@daltonmeon @damienhe @polygres @fdotinc so cool!

Profilbild von dale

dalevor 1 Monat

@daltonmeon @damienhe @polygres @fdotinc Thanks manny!

Profilbild von Massi

Massivor 1 Monat

@daltonmeon @damienhe @polygres @fdotinc so cool!!

Profilbild von dale

dalevor 1 Monat

@daltonmeon @damienhe @polygres @fdotinc Ty!

Profilbild von Andrey

Andreyvor 1 Monat

@daltonmeon @damienhe @polygres @fdotinc this is fkn legendary

Profilbild von dale

dalevor 1 Monat

@daltonmeon @damienhe @polygres @fdotinc Hehehee

Profilbild von gz

gzvor 1 Monat

@daltonmeon @damienhe @polygres @fdotinc 🚀

Profilbild von dale

dalevor 1 Monat

@daltonmeon @damienhe @polygres @fdotinc Hehehe

Profilbild von Bambambam

Bambambamvor 1 Monat

@daltonmeon @damienhe @polygres @fdotinc Happy for you bro

Profilbild von dale

dalevor 1 Monat

@daltonmeon @damienhe @polygres @fdotinc Thank you!

Profilbild von Jason Jin

Jason Jinvor 1 Monat

@daltonmeon @damienhe @polygres @fdotinc goatttt

Profilbild von dale

dalevor 1 Monat

@daltonmeon @damienhe @polygres @fdotinc Hehe see you later!

Profilbild von Nour Zahzah

Nour Zahzahvor 1 Monat

@daltonmeon @damienhe @polygres @fdotinc nice

Profilbild von dale

dalevor 1 Monat

@daltonmeon @damienhe @polygres @fdotinc Thank you!

Profilbild von DylanBryan

DylanBryanvor 1 Monat

@daltonmeon @damienhe @polygres @fdotinc Much needed

Profilbild von dale

dalevor 1 Monat

@daltonmeon @damienhe @polygres @fdotinc LFG!

Profilbild von Wass

Wassvor 1 Monat

@daltonmeon @damienhe @polygres @fdotinc The zero-sync point matters. The moment agents depend on a copied index, the copy becomes another operational liability. Keeping Postgres as the source of truth makes this much more interesting.

Profilbild von dale

dalevor 1 Monat

@daltonmeon @damienhe @polygres @fdotinc yeah

Profilbild von Hemant

Hemantvor 1 Monat

@daltonmeon @damienhe @polygres @fdotinc its a revolution!!

Profilbild von dale

dalevor 1 Monat

@daltonmeon @damienhe @polygres @fdotinc It is!

Profilbild von Sheral Tech

Sheral Techvor 1 Monat

@daltonmeon @damienhe @polygres @fdotinc Clean solution one database every search type zero migration headaches

Profilbild von Kingsley Uyi Idehen

Kingsley Uyi Idehenvor 1 Monat

@daltonmeon @damienhe @polygres @fdotinc Simple question, circa 2026 in the age of AI: Why is it DBMS specific when open standards like ODBC and JDBC exist?

Profilbild von Rain Miao

Rain Miaovor 1 Monat

@daltonmeon @damienhe @polygres @fdotinc the hair style looks sick

Profilbild von dale

dalevor 1 Monat

@daltonmeon @damienhe @polygres @fdotinc 😭 it’s just my hair grown out after 6 weeks

Profilbild von Rain Miao

Rain Miaovor 1 Monat

@daltonmeon @damienhe @polygres @fdotinc I envy your hairline

Profilbild von dale

dalevor 1 Monat

@daltonmeon @damienhe @polygres @fdotinc I have a big ass forehead lowk

Profilbild von Veljko Kovac

Veljko Kovacvor 1 Monat

@daltonmeon @damienhe @polygres @fdotinc cool work

Profilbild von dale

dalevor 1 Monat

@daltonmeon @damienhe @polygres @fdotinc Thank you!

Profilbild von MAFIA BOD

MAFIA BODvor 1 Monat

@daltonmeon @damienhe @polygres @fdotinc Legend ⭐

Profilbild von dale

dalevor 1 Monat

@daltonmeon @damienhe @polygres @fdotinc Hehehe

Profilbild von Nima Mozhgani 🫐

Nima Mozhgani 🫐vor 1 Monat

@daltonmeon @damienhe @polygres @fdotinc Oh heck ya this looks great!

Profilbild von dale

dalevor 1 Monat

@daltonmeon @damienhe @polygres @fdotinc try it out!

Profilbild von Adityaa

Adityaavor 1 Monat

@daltonmeon @damienhe @polygres @fdotinc Goated

Profilbild von dale

dalevor 1 Monat

@daltonmeon @damienhe @polygres @fdotinc g bro

Profilbild von Annalea L

Annalea Lvor 1 Monat

@daltonmeon @damienhe @polygres @fdotinc Polygres is changing how juwels memory functions.

Profilbild von dale

dalevor 1 Monat

@daltonmeon @damienhe @polygres @fdotinc see you later!

Profilbild von Annalea L

Annalea Lvor 1 Monat

@daltonmeon @damienhe @polygres @fdotinc YES!

Profilbild von Aakash Vidiyala

Aakash Vidiyalavor 1 Monat

@daltonmeon @damienhe @polygres @fdotinc Omw to try this goated product

Profilbild von dale

dalevor 1 Monat

@daltonmeon @damienhe @polygres @fdotinc deploy it today!

Profilbild von Taimoor Hassan 🇺🇸🇵🇰

Taimoor Hassan 🇺🇸🇵🇰vor 1 Monat

@daltonmeon @damienhe @polygres @fdotinc 🔥🔥🔥🔥

Profilbild von dale

dalevor 1 Monat

@daltonmeon @damienhe @polygres @fdotinc hehehee it was great to meet you taimoor!

Profilbild von Taimoor Hassan 🇺🇸🇵🇰

Taimoor Hassan 🇺🇸🇵🇰vor 1 Monat

@daltonmeon @damienhe @polygres @fdotinc Likewise brotha 🫡

Profilbild von Ander Vallejo

Ander Vallejovor 1 Monat

@daltonmeon @damienhe @polygres @fdotinc LFG

Profilbild von dale

dalevor 1 Monat

@daltonmeon @damienhe @polygres @fdotinc Hehehe

Profilbild von Leo Oliemans | Refinery

Leo Oliemans | Refineryvor 1 Monat

@daltonmeon @damienhe @polygres @fdotinc Search over Postgres is getting easier. The operational wrinkle is what happens when the underlying row changes between retrieval and action. Do you expose freshness or conflict signals to the workflow, or leave that to each consumer?

Profilbild von Jay @ LegalOS

Jay @ LegalOSvor 1 Monat

@daltonmeon @damienhe @polygres @fdotinc i wouldnt fade this is if i were you!

Profilbild von dale

dalevor 1 Monat

@daltonmeon @damienhe @polygres @fdotinc theres a reddish light behind me my hair is naturally super dark brown

Profilbild von Jay @ LegalOS

Jay @ LegalOSvor 1 Monat

@daltonmeon @damienhe @polygres @fdotinc bro pls

Profilbild von Michel aka Agent B

Michel aka Agent Bvor 1 Monat

@daltonmeon @damienhe @polygres @fdotinc Inteersting Dale ! Bravo to the team 👏👏

Profilbild von dale

dalevor 1 Monat

@daltonmeon @damienhe @polygres @fdotinc thank you!

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marcus

50,034 Aufrufe • vor 27 Tagen

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This is, by far, one of the best uses of modern AI. If you don't use embeddings when querying your database, you are definitely leaving a lot on the table. In this video, I'll show you how to run semantic searches using OpenAI and PostgreSQL. It's all thanks to Pgai, an open-source PostgreSQL extension: Here's what will happen: 1. We'll create a simple table with news articles 2. We'll generate embeddings for those articles 3. We'll run queries on top of those embeddings For this video, I generated the embeddings using a simple query, but pgai Vectorizer would do the same automatically as new information makes it into the database. This is awesome! If you have a PostgreSQL database with data you are searching over, you should start experimenting with semantic searches immediately. For most use cases, a combination of full-text search + semantic search is the best approach. If you don't have a PostgreSQL database around, you can try free for 30 days using Timescale: Thanks to the TimescaleDB (by Tiger Data) team for partnering with me on this post!

Santiago

110,133 Aufrufe • vor 1 Jahr

Finally, a database can generate its own embeddings now. When you add semantic search to an app, the standard step is to wire up an external embedding service, sync the vectors to a separate store, and write glue to keep everything updated as data changes. While this is a standard setup, problems show up later when your data changes, but your pipeline has already run, and search quality starts degrading in ways that are hard to pin down because nothing is explicitly broken. MongoDB Atlas handles this with auto-embedding. You point an index at a text field, specify a Voyage AI model, and Atlas generates the vectors inside the database. When a document changes, it re-embeds itself automatically, so that the search always stays grounded to the current context. I tested this against MongoDB's sample movies collection, 21,000 plots indexed with one config. A plain English query about dystopian machine-controlled futures returned semantically relevant results, despite having no keyword overlap. You can try it yourself here: One index config replaced the embedding service, the vector store, and the sync layer you used to maintain separately. The video below depicts this in action, and I worked with the MongoDB team to put this together. I have also put together a full LLM engineering roadmap covering what it actually takes to build and ship production AI systems in 2026. Read it below.

Finally, a database can generate its own embeddings now. When you add semantic search to an app, the standard step is to wire up an external embedding service, sync the vectors to a separate store, and write glue to keep everything updated as data changes. While this is a standard setup, problems show up later when your data changes, but your pipeline has already run, and search quality starts degrading in ways that are hard to pin down because nothing is explicitly broken. MongoDB Atlas handles this with auto-embedding. You point an index at a text field, specify a Voyage AI model, and Atlas generates the vectors inside the database. When a document changes, it re-embeds itself automatically, so that the search always stays grounded to the current context. I tested this against MongoDB's sample movies collection, 21,000 plots indexed with one config. A plain English query about dystopian machine-controlled futures returned semantically relevant results, despite having no keyword overlap. You can try it yourself here: One index config replaced the embedding service, the vector store, and the sync layer you used to maintain separately. The video below depicts this in action, and I worked with the MongoDB team to put this together. I have also put together a full LLM engineering roadmap covering what it actually takes to build and ship production AI systems in 2026. Read it below.

Avi Chawla

14,463 Aufrufe • vor 2 Monaten

Your AI Agents deserve something More Powerful Than Vector DB Every developer building AI agents right now is using a vector database. Embed your data, cosine similarity, retrieve, done. And it works... until it doesn't. HydraDB is trying to solve the problem, so I am making a series of why it's important over the course of next few videos.

Your AI Agents deserve something More Powerful Than Vector DB Every developer building AI agents right now is using a vector database. Embed your data, cosine similarity, retrieve, done. And it works... until it doesn't. HydraDB is trying to solve the problem, so I am making a series of why it's important over the course of next few videos.

Harnoor Singh

13,367 Aufrufe • vor 6 Monaten

I can search a database with 32 million records in less than 50 milliseconds. This speed is insane! There's no reason your search is slow. There's no reason you aren't using this library when building applications with a lot of data. Take a look at typesense |. It's an open-source search engine. It's crazy fast and easy to use. They are sponsoring this post. Here is their GitHub repository: You can use the library in Python, JavaScript, PHP, Ruby, Swift, and pretty much any other programming language out there. Here are some of the things you can do with Typesense: • Vector search over embeddings • Conversational search for RAG applications • Image similarity search • Voice search And it does all of this extremely fast. A few interesting notes about the library: • Optimized for developer happiness • Works out of the box for most use cases • Very intuitive API • Easy to deploy and scale Probably the best way to compare them is against ElasticSearch. The latter is much more complex to configure and run with a steep learning curve. Check their GitHub repository. They have examples, tutorials, and everything you need.

I can search a database with 32 million records in less than 50 milliseconds. This speed is insane! There's no reason your search is slow. There's no reason you aren't using this library when building applications with a lot of data. Take a look at typesense |. It's an open-source search engine. It's crazy fast and easy to use. They are sponsoring this post. Here is their GitHub repository: You can use the library in Python, JavaScript, PHP, Ruby, Swift, and pretty much any other programming language out there. Here are some of the things you can do with Typesense: • Vector search over embeddings • Conversational search for RAG applications • Image similarity search • Voice search And it does all of this extremely fast. A few interesting notes about the library: • Optimized for developer happiness • Works out of the box for most use cases • Very intuitive API • Easy to deploy and scale Probably the best way to compare them is against ElasticSearch. The latter is much more complex to configure and run with a steep learning curve. Check their GitHub repository. They have examples, tutorials, and everything you need.

Santiago

160,154 Aufrufe • vor 2 Jahren

Evernote Search just got a lot more powerful! No more slogging through endless notes or wracking your brain for the perfect keyword. With AI-Powered Search, all you have to do is ask. 🐘 Try it yourself! Download Evernote today:

Evernote Search just got a lot more powerful! No more slogging through endless notes or wracking your brain for the perfect keyword. With AI-Powered Search, all you have to do is ask. 🐘 Try it yourself! Download Evernote today:

Evernote

29,921 Aufrufe • vor 2 Jahren

So you forgot how you fixed that bug the other day. 😬 No worries. GitHub Copilot CLI remembers. With its local SQLite database of your session history (and a full-text search index), you can instantly pull up context from your previous work. ✅ Install Copilot CLI and save brain space. ⬇️

So you forgot how you fixed that bug the other day. 😬 No worries. GitHub Copilot CLI remembers. With its local SQLite database of your session history (and a full-text search index), you can instantly pull up context from your previous work. ✅ Install Copilot CLI and save brain space. ⬇️

GitHub

47,229 Aufrufe • vor 7 Monaten